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Comparative translating ribosome affinity purification-RNAseq (TRAPseq) by lung cell-specific drivers

GSE250140 Mus musculus (mouse) RNA-seq (gene expression) 49 samples Submitted 2025/12/01 Platform GPL19057
At a glance

RNA-seq (gene expression) · mouse · 49 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Mus musculus (mouse)
Samples
49 samples: 44 distinct titles, 5 of them repeatedGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Summary (written by the submitters)
Translating ribosome affinity purification coupled to next-generation RNA sequencing permits profiling of specific cellular compartments. Here, we used well-characterized Cre and CreER drivers to lineage trace different cell populations in the developing lung with a ribosomal L10-GFP fusion protein. Two different anti-GFP antibodies were used to pull down ribosomes.
Read the full summary ↓
mRNA was isolated, reversed transcribed, and processed using next-generation sequencing. Profiling showed distinct clustering between alveolar cell drivers and epithelial cell drivers, which were clearly distinguishable from raw mRNA input.
This dataset
Open GSE250140 in finder → Find more mouse RNA-seq datasets →
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE250140_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1052095 and SRA study SRP477856. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
44 distinct titles, 5 of them repeated
TWhL-L10LyveC ×2 TWhL-L10LyveRb ×2 TWhL-L10ProxRb ×2 TWhL-L10TCF21C ×2 TWhL-L10TCF21Rb ×2

How we read this: Read from 49 sample titles: 44 distinct titles with little repetition. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study

+ 9 more — browse all 49 samples with per-sample file links →

Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.